{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# YOLO11 Relax 优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 416x640 1 car, 15.4ms\n",
      "Speed: 2.7ms preprocess, 15.4ms inference, 2.2ms postprocess per image at shape (1, 3, 416, 640)\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=320x208>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "from ultralytics import YOLO\n",
    "\n",
    "input_path = \"images/vehicle-jaguar-f-type-car-red-cars-wallpaper.jpg\"\n",
    "im = Image.open(input_path) #.resize((384, 640))\n",
    "yolo = YOLO(\"yolo11n.pt\")\n",
    "results = yolo(np.array(im), conf=0.25)\n",
    "Image.fromarray(results[0].plot()).resize((320, 208))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据内存的连续性：True\n",
      "数据内存的连续性(transpose)：False\n",
      "数据内存的连续性：True\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=640x320>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import torch\n",
    "from ultralytics.data.augment import LetterBox\n",
    "\n",
    "imgsz = 640, 640\n",
    "strides = yolo.model.stride\n",
    "mean = (0,)\n",
    "std = (255,)\n",
    "\n",
    "letterbox = LetterBox(new_shape=imgsz, auto=False, scaleFill=False, scaleup=True, stride=32)\n",
    "origin_image = np.asanyarray(Image.open(input_path))\n",
    "letterbox_image = letterbox(image=origin_image)\n",
    "xs = np.stack([letterbox_image - mean])\n",
    "print(f\"数据内存的连续性：{xs.flags[\"C_CONTIGUOUS\"]}\")\n",
    "xs = xs.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)\n",
    "print(f\"数据内存的连续性(transpose)：{xs.flags[\"C_CONTIGUOUS\"]}\")\n",
    "xs = np.ascontiguousarray(xs)  # contiguous\n",
    "print(f\"数据内存的连续性：{xs.flags[\"C_CONTIGUOUS\"]}\")\n",
    "xs = (xs / std).astype(\"float32\") # 归一化值域范围为 0.0 - 1.0\n",
    "Image.fromarray(\n",
    "    np.concatenate([letterbox_image, (xs[0]*std).astype(\"uint8\").transpose((1, 2, 0))], axis=1)\n",
    ").resize((640, 320,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "后处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics.utils import ops\n",
    "from ultralytics.engine.results import Results\n",
    "\n",
    "def postprocess(preds, img, orig_imgs, names, input_path, conf_thres=0.25, iou_thres=0.45,):\n",
    "    \"\"\"Post-processes predictions and returns a list of Results objects.\"\"\"\n",
    "    preds = ops.non_max_suppression(\n",
    "        preds,\n",
    "        conf_thres=conf_thres,\n",
    "        iou_thres=iou_thres,\n",
    "        # agnostic=self.args.agnostic_nms,\n",
    "        # max_det=self.args.max_det,\n",
    "        # classes=80,\n",
    "    )\n",
    "\n",
    "    results = []\n",
    "    for i, pred in enumerate(preds):\n",
    "        orig_img = orig_imgs[i]\n",
    "        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)\n",
    "        img_path = input_path\n",
    "        results.append(Results(orig_img, path=img_path, names=names, boxes=pred))\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ONNX 推理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import onnxruntime\n",
    "import onnx\n",
    "onnx_model = onnx.load('yolo11n.onnx')\n",
    "# 通过 ONNX 运行模型以获取预期结果\n",
    "ort_session = onnxruntime.InferenceSession(\n",
    "    onnx_model.SerializeToString(), providers=[\"CPUExecutionProvider\"]\n",
    ")\n",
    "inputs = {\"images\": xs}\n",
    "ort_output = ort_session.run([], inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试 YOLO ONNX Relax 前端"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tvm\n",
    "from tvm import relax\n",
    "from tvm.relax.frontend.onnx import from_onnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvm_model = from_onnx(onnx_model, keep_params_in_input=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_actual = relax.transform.AnnotateTIROpPattern()(tvm_model)\n",
    "mod_actual = relax.transform.FuseOps()(mod_actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
       "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
       "<span style=\"color: #007979; font-style: italic\"># from tvm.script import relax as R</span>\n",
       "\n",
       "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
       "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
       "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func(private<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">True</span>)\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">split</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">16</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_1: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">16</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
       "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;op_pattern&quot;</span>: <span style=\"color: #008000\">2</span>, <span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
       "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">16</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2, v_ax3]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">16</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">160</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_1&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">16</span>), v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_1[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split_1[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">16</span>), v_ax2, v_ax3]\n",
       "\n",
       "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func(private<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">True</span>)\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">split1</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_1: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
       "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;op_pattern&quot;</span>: <span style=\"color: #008000\">2</span>, <span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
       "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2, v_ax3]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_1&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_1[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split_1[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), v_ax2, v_ax3]\n",
       "\n",
       "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func(private<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">True</span>)\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">split2</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_1: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
       "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;op_pattern&quot;</span>: <span style=\"color: #008000\">2</span>, <span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
       "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2, v_ax3]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">40</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_1&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_1[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split_1[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), v_ax2, v_ax3]\n",
       "\n",
       "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func(private<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">True</span>)\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">split3</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">256</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_1: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
       "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;op_pattern&quot;</span>: <span style=\"color: #008000\">2</span>, <span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
       "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2, v_ax3]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">20</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_1&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_1[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split_1[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), v_ax2, v_ax3]\n",
       "\n",
       "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func(private<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">True</span>)\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">split4</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">128</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_1: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_2: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
       "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;op_pattern&quot;</span>: <span style=\"color: #008000\">2</span>, <span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
       "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2, v_ax3]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_1&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_1[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split_1[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), v_ax3]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2, ax3 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">2</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">400</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_2&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2, v_ax3 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSSS&quot;</span>, [ax0, ax1, ax2, ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), v_ax3])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_2[v_ax0, v_ax1, v_ax2, v_ax3])\n",
       "                T_split_2[v_ax0, v_ax1, v_ax2, v_ax3] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), v_ax3]\n",
       "\n",
       "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func(private<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">True</span>)\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">split5</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">144</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">8400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">8400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), T_split_1: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">8400</span>)), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
       "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;op_pattern&quot;</span>: <span style=\"color: #008000\">2</span>, <span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
       "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">8400</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSS&quot;</span>, [ax0, ax1, ax2])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1, v_ax2])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split[v_ax0, v_ax1, v_ax2])\n",
       "                T_split[v_ax0, v_ax1, v_ax2] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1, v_ax2]\n",
       "        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1, ax2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">80</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">8400</span>)):\n",
       "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;T_split_1&quot;</span>):\n",
       "                v_ax0, v_ax1, v_ax2 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>remap(<span style=\"color: #BA2121\">&quot;SSS&quot;</span>, [ax0, ax1, ax2])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), v_ax2])\n",
       "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(T_split_1[v_ax0, v_ax1, v_ax2])\n",
       "                T_split_1[v_ax0, v_ax1, v_ax2] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v_ax0, v_ax1 <span style=\"color: #AA22FF; font-weight: bold\">+</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">64</span>), v_ax2]\n",
       "\n",
       "    <span style=\"color: #AA22FF\">@R</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>function\n",
       "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(images: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">3</span>, <span style=\"color: #008000\">640</span>, <span style=\"color: #008000\">640</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)) <span style=\"color: #AA22FF; font-weight: bold\">-&gt;</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">84</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>):\n",
       "        R<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;num_input&quot;</span>: <span style=\"color: #008000\">1</span>})\n",
       "        cls <span style=\"color: #AA22FF; font-weight: bold\">=</span> Module\n",
       "        <span style=\"color: #008000; font-weight: bold\">with</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>dataflow():\n",
       "            lv: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">320</span>, <span style=\"color: #008000\">320</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(images, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">0</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv1: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">1</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv2: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">320</span>, <span style=\"color: #008000\">320</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv, lv1)\n",
       "            lv3: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">320</span>, <span style=\"color: #008000\">320</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv2)\n",
       "            lv4: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">320</span>, <span style=\"color: #008000\">320</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv2, lv3)\n",
       "            lv5: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv4, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">2</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv6: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">3</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv7: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv5, lv6)\n",
       "            lv8: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv7)\n",
       "            lv9: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv7, lv8)\n",
       "            lv10: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv9, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">4</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv11: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">5</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv12: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv10, lv11)\n",
       "            lv13: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv12)\n",
       "            lv14: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv12, lv13)\n",
       "            lv15 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split, (lv14,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv16: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv15[<span style=\"color: #008000\">0</span>]\n",
       "            lv17: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv15[<span style=\"color: #008000\">1</span>]\n",
       "            lv18: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv17, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">6</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv19: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">7</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv20: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv18, lv19)\n",
       "            lv21: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv20)\n",
       "            lv22: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv20, lv21)\n",
       "            lv23: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv22, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">8</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv24: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">9</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv25: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv23, lv24)\n",
       "            lv26: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv25)\n",
       "            lv27: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv25, lv26)\n",
       "            lv28: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv17, lv27)\n",
       "            lv29: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">48</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv16, lv17, lv28), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv30: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv29, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">10</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv31: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">11</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv32: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv30, lv31)\n",
       "            lv33: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv32)\n",
       "            lv34: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">160</span>, <span style=\"color: #008000\">160</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv32, lv33)\n",
       "            lv35: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv34, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">12</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv36: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">13</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv37: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv35, lv36)\n",
       "            lv38: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv37)\n",
       "            lv39: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv37, lv38)\n",
       "            lv40: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv39, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">14</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv41: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">15</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv42: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv40, lv41)\n",
       "            lv43: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv42)\n",
       "            lv44: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv42, lv43)\n",
       "            lv45 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split1, (lv44,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv46: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv45[<span style=\"color: #008000\">0</span>]\n",
       "            lv47: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv45[<span style=\"color: #008000\">1</span>]\n",
       "            lv48: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv47, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">16</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv49: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">17</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv50: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv48, lv49)\n",
       "            lv51: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv50)\n",
       "            lv52: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv50, lv51)\n",
       "            lv53: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv52, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">18</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv54: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">19</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv55: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv53, lv54)\n",
       "            lv56: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv55)\n",
       "            lv57: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv55, lv56)\n",
       "            lv58: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv47, lv57)\n",
       "            lv59: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">96</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv46, lv47, lv58), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv60: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv59, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">20</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv61: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">21</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv62: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv60, lv61)\n",
       "            lv63: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv62)\n",
       "            lv64: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv62, lv63)\n",
       "            lv65: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv64, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">22</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv66: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">23</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv67: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv65, lv66)\n",
       "            lv68: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv67)\n",
       "            lv69: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv67, lv68)\n",
       "            lv70: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv69, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">24</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv71: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">25</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv72: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv70, lv71)\n",
       "            lv73: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv72)\n",
       "            lv74: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv72, lv73)\n",
       "            lv75 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split2, (lv74,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv76: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv75[<span style=\"color: #008000\">0</span>]\n",
       "            lv77: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv75[<span style=\"color: #008000\">1</span>]\n",
       "            lv78: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv77, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">26</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv79: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">27</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv80: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv77, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">28</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv81: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">29</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv82: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv78, lv79)\n",
       "            lv83: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv80, lv81)\n",
       "            lv84: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv82)\n",
       "            lv85: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv83)\n",
       "            lv86: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv82, lv84)\n",
       "            lv87: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv86, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">30</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv88: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">31</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv89: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv87, lv88)\n",
       "            lv90: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv89)\n",
       "            lv91: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv89, lv90)\n",
       "            lv92: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv91, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">32</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv93: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">33</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv94: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv92, lv93)\n",
       "            lv95: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv94)\n",
       "            lv96: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv94, lv95)\n",
       "            lv97: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv86, lv96)\n",
       "            lv98: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv97, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">34</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv99: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">35</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv100: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv98, lv99)\n",
       "            lv101: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv100)\n",
       "            lv102: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv100, lv101)\n",
       "            lv103: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv102, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">36</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv104: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">37</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv105: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv103, lv104)\n",
       "            lv106: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv105)\n",
       "            lv107: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv105, lv106)\n",
       "            lv108: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv97, lv107)\n",
       "            lv109: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv83, lv85)\n",
       "            lv110: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv108, lv109), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv111: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv110, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">38</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv112: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">39</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv113: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv111, lv112)\n",
       "            lv114: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv113)\n",
       "            lv115: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv113, lv114)\n",
       "            lv116: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">192</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv76, lv77, lv115), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv117: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv116, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">40</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv118: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">41</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv119: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv117, lv118)\n",
       "            lv120: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv119)\n",
       "            lv121: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv119, lv120)\n",
       "            lv122: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv121, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">42</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv123: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">43</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv124: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv122, lv123)\n",
       "            lv125: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv124)\n",
       "            lv126: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv124, lv125)\n",
       "            lv127: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv126, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">44</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv128: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">45</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv129: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv127, lv128)\n",
       "            lv130: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv129)\n",
       "            lv131: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv129, lv130)\n",
       "            lv132 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split3, (lv131,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv133: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv132[<span style=\"color: #008000\">0</span>]\n",
       "            lv134: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv132[<span style=\"color: #008000\">1</span>]\n",
       "            lv135: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv134, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">46</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv136: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">47</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv137: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv134, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">48</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv138: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">49</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv139: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv135, lv136)\n",
       "            lv140: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv137, lv138)\n",
       "            lv141: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv139)\n",
       "            lv142: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv140)\n",
       "            lv143: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv139, lv141)\n",
       "            lv144: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv143, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">50</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv145: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">51</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv146: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv144, lv145)\n",
       "            lv147: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv146)\n",
       "            lv148: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv146, lv147)\n",
       "            lv149: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv148, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">52</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv150: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">53</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv151: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv149, lv150)\n",
       "            lv152: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv151)\n",
       "            lv153: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv151, lv152)\n",
       "            lv154: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv143, lv153)\n",
       "            lv155: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv154, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">54</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv156: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">55</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv157: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv155, lv156)\n",
       "            lv158: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv157)\n",
       "            lv159: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv157, lv158)\n",
       "            lv160: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv159, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">56</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv161: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">57</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv162: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv160, lv161)\n",
       "            lv163: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv162)\n",
       "            lv164: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv162, lv163)\n",
       "            lv165: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv154, lv164)\n",
       "            lv166: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv140, lv142)\n",
       "            lv167: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv165, lv166), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv168: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv167, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">58</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv169: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">59</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv170: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv168, lv169)\n",
       "            lv171: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv170)\n",
       "            lv172: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv170, lv171)\n",
       "            lv173: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">384</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv133, lv134, lv172), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv174: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv173, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">60</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv175: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">61</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv176: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv174, lv175)\n",
       "            lv177: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv176)\n",
       "            lv178: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv176, lv177)\n",
       "            lv179: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv178, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">62</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv180: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">63</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv181: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv179, lv180)\n",
       "            lv182: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv181)\n",
       "            lv183: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv181, lv182)\n",
       "            lv184: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>max_pool2d(lv183, pool_size<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">5</span>, <span style=\"color: #008000\">5</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], ceil_mode<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>, count_include_pad<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>, layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>)\n",
       "            lv185: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>max_pool2d(lv184, pool_size<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">5</span>, <span style=\"color: #008000\">5</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], ceil_mode<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>, count_include_pad<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>, layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>)\n",
       "            lv186: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>max_pool2d(lv185, pool_size<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">5</span>, <span style=\"color: #008000\">5</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], ceil_mode<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>, count_include_pad<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>, layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>)\n",
       "            lv187: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">512</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv183, lv184, lv185, lv186), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv188: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv187, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">64</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv189: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">65</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv190: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv188, lv189)\n",
       "            lv191: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv190)\n",
       "            lv192: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv190, lv191)\n",
       "            lv193: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv192, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">66</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv194: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">67</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv195: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv193, lv194)\n",
       "            lv196: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv195)\n",
       "            lv197: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv195, lv196)\n",
       "            lv198 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split3, (lv197,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv199: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv198[<span style=\"color: #008000\">0</span>]\n",
       "            lv200: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv198[<span style=\"color: #008000\">1</span>]\n",
       "            lv201: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv200, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">68</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv202: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">69</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv203: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv201, lv202)\n",
       "            lv204: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv203, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">400</span>]))\n",
       "            lv205 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split4, (lv204,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv206: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv205[<span style=\"color: #008000\">0</span>]\n",
       "            lv207: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv205[<span style=\"color: #008000\">1</span>]\n",
       "            lv208: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv205[<span style=\"color: #008000\">2</span>]\n",
       "            lv209: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">400</span>, <span style=\"color: #008000\">32</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>permute_dims(lv206, axes<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">3</span>, <span style=\"color: #008000\">2</span>])\n",
       "            lv210: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv208, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>]))\n",
       "            lv211: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv210, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">70</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">128</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv212: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">71</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv213: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">400</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>matmul(lv209, lv207, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv214: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">400</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv213, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>const(<span style=\"color: #008000\">0.1767766922712326</span>, <span style=\"color: #BA2121\">&quot;float32&quot;</span>))\n",
       "            lv215: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">400</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>softmax(lv214, axis<span style=\"color: #AA22FF; font-weight: bold\">=-</span><span style=\"color: #008000\">1</span>)\n",
       "            lv216: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">400</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>permute_dims(lv215, axes<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">3</span>, <span style=\"color: #008000\">2</span>])\n",
       "            lv217: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>matmul(lv208, lv216, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv218: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv217, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>]))\n",
       "            lv219: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv211, lv212)\n",
       "            lv220: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv218, lv219)\n",
       "            lv221: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv220, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">72</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv222: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">73</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv223: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv221, lv222)\n",
       "            lv224: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv200, lv223)\n",
       "            lv225: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv224, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">74</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv226: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">75</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv227: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv225, lv226)\n",
       "            lv228: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv227)\n",
       "            lv229: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv227, lv228)\n",
       "            lv230: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv229, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">76</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv231: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">77</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv232: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv230, lv231)\n",
       "            lv233: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv224, lv232)\n",
       "            lv234: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv199, lv233), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv235: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv234, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">78</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv236: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">79</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv237: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv235, lv236)\n",
       "            lv238: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv237)\n",
       "            lv239: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv237, lv238)\n",
       "            lv240: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>image<span style=\"color: #AA22FF; font-weight: bold\">.</span>resize2d(lv239, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>]), roi<span style=\"color: #AA22FF; font-weight: bold\">=</span>[T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>)], layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, method<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;nearest_neighbor&quot;</span>, coordinate_transformation_mode<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;asymmetric&quot;</span>, rounding_method<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;floor&quot;</span>, cubic_alpha<span style=\"color: #AA22FF; font-weight: bold\">=-</span><span style=\"color: #008000\">0.75</span>, cubic_exclude<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">0</span>, extrapolation_value<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">0.0</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv241: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">384</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv240, lv121), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv242: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv241, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">80</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv243: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">81</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv244: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv242, lv243)\n",
       "            lv245: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv244)\n",
       "            lv246: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv244, lv245)\n",
       "            lv247 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split2, (lv246,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv248: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv247[<span style=\"color: #008000\">0</span>]\n",
       "            lv249: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv247[<span style=\"color: #008000\">1</span>]\n",
       "            lv250: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv249, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">82</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv251: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">83</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv252: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv250, lv251)\n",
       "            lv253: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv252)\n",
       "            lv254: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv252, lv253)\n",
       "            lv255: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv254, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">84</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv256: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">85</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv257: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv255, lv256)\n",
       "            lv258: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv257)\n",
       "            lv259: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv257, lv258)\n",
       "            lv260: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv249, lv259)\n",
       "            lv261: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">192</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv248, lv249, lv260), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv262: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv261, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">86</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv263: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">87</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv264: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv262, lv263)\n",
       "            lv265: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv264)\n",
       "            lv266: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv264, lv265)\n",
       "            lv267: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>image<span style=\"color: #AA22FF; font-weight: bold\">.</span>resize2d(lv266, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>]), roi<span style=\"color: #AA22FF; font-weight: bold\">=</span>[T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>), T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0.0</span>)], layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, method<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;nearest_neighbor&quot;</span>, coordinate_transformation_mode<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;asymmetric&quot;</span>, rounding_method<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;floor&quot;</span>, cubic_alpha<span style=\"color: #AA22FF; font-weight: bold\">=-</span><span style=\"color: #008000\">0.75</span>, cubic_exclude<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">0</span>, extrapolation_value<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">0.0</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv268: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv267, lv64), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv269: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv268, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">88</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv270: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">89</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv271: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv269, lv270)\n",
       "            lv272: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv271)\n",
       "            lv273: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv271, lv272)\n",
       "            lv274 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split1, (lv273,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv275: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv274[<span style=\"color: #008000\">0</span>]\n",
       "            lv276: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv274[<span style=\"color: #008000\">1</span>]\n",
       "            lv277: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv276, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">90</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv278: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">91</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv279: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv277, lv278)\n",
       "            lv280: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv279)\n",
       "            lv281: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv279, lv280)\n",
       "            lv282: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv281, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">92</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv283: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">93</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv284: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv282, lv283)\n",
       "            lv285: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv284)\n",
       "            lv286: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv284, lv285)\n",
       "            lv287: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv276, lv286)\n",
       "            lv288: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">96</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv275, lv276, lv287), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv289: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv288, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">94</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv290: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">95</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv291: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv289, lv290)\n",
       "            lv292: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv291)\n",
       "            lv293: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv291, lv292)\n",
       "            lv294: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv293, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">96</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv295: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">97</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv296: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv293, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">98</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv297: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">99</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv298: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv293, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">100</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">64</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv299: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">101</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv300: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv294, lv295)\n",
       "            lv301: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv296, lv297)\n",
       "            lv302: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv298, lv299)\n",
       "            lv303: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv300)\n",
       "            lv304: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv301)\n",
       "            lv305: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv302)\n",
       "            lv306: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv300, lv303)\n",
       "            lv307: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv301, lv304)\n",
       "            lv308: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv307, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">102</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv309: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">103</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv310: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv302, lv305)\n",
       "            lv311: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv310, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">104</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv312: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">105</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv313: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">192</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv306, lv266), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv314: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv313, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">106</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv315: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">107</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv316: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv308, lv309)\n",
       "            lv317: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv311, lv312)\n",
       "            lv318: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv314, lv315)\n",
       "            lv319: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv316)\n",
       "            lv320: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv317)\n",
       "            lv321: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv318)\n",
       "            lv322: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv316, lv319)\n",
       "            lv323: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv322, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">108</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv324: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">109</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv325: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv317, lv320)\n",
       "            lv326: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv325, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">110</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">80</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv327: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">111</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv328: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv318, lv321)\n",
       "            lv329 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split2, (lv328,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv330: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv329[<span style=\"color: #008000\">0</span>]\n",
       "            lv331: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv329[<span style=\"color: #008000\">1</span>]\n",
       "            lv332: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv326, lv327)\n",
       "            lv333: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv331, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">112</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv334: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">113</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv335: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv332)\n",
       "            lv336: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv333, lv334)\n",
       "            lv337: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv332, lv335)\n",
       "            lv338: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv337, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">114</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv339: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">115</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv340: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv336)\n",
       "            lv341: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv338, lv339)\n",
       "            lv342: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv336, lv340)\n",
       "            lv343: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv342, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">116</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv344: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">117</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv345: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv341)\n",
       "            lv346: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv343, lv344)\n",
       "            lv347: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv341, lv345)\n",
       "            lv348: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv347, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">118</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv349: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">119</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv350: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv346)\n",
       "            lv351: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv323, lv324)\n",
       "            lv352: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv348, lv349)\n",
       "            lv353: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv346, lv350)\n",
       "            lv354: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">80</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv351, lv352), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv355: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv331, lv353)\n",
       "            lv356: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">192</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv330, lv331, lv355), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv357: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv356, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">120</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv358: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">121</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv359: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv357, lv358)\n",
       "            lv360: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv359)\n",
       "            lv361: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv359, lv360)\n",
       "            lv362: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv361, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">122</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">2</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv363: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">123</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv364: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv361, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">124</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv365: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">125</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv366: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv361, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">126</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">128</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv367: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">127</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv368: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv362, lv363)\n",
       "            lv369: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv364, lv365)\n",
       "            lv370: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv366, lv367)\n",
       "            lv371: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv368)\n",
       "            lv372: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv369)\n",
       "            lv373: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv370)\n",
       "            lv374: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv368, lv371)\n",
       "            lv375: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv369, lv372)\n",
       "            lv376: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv375, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">128</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv377: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">129</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv378: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv370, lv373)\n",
       "            lv379: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv378, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">130</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv380: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">131</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv381: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">384</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv374, lv239), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv382: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv381, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">132</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv383: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">133</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv384: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv376, lv377)\n",
       "            lv385: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv379, lv380)\n",
       "            lv386: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv382, lv383)\n",
       "            lv387: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv384)\n",
       "            lv388: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv385)\n",
       "            lv389: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv386)\n",
       "            lv390: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv384, lv387)\n",
       "            lv391: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv390, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">134</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv392: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">135</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv393: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv385, lv388)\n",
       "            lv394: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv393, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">136</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">80</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv395: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">137</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv396: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv386, lv389)\n",
       "            lv397 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split3, (lv396,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv398: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv397[<span style=\"color: #008000\">0</span>]\n",
       "            lv399: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv397[<span style=\"color: #008000\">1</span>]\n",
       "            lv400: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv394, lv395)\n",
       "            lv401: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv399, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">138</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv402: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">139</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv403: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv399, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">140</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv404: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">141</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv405: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv400)\n",
       "            lv406: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv401, lv402)\n",
       "            lv407: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv403, lv404)\n",
       "            lv408: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv400, lv405)\n",
       "            lv409: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv408, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">142</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv410: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">143</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv411: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv406)\n",
       "            lv412: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv407)\n",
       "            lv413: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv409, lv410)\n",
       "            lv414: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv406, lv411)\n",
       "            lv415: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv414, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">144</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv416: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">145</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv417: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv413)\n",
       "            lv418: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv415, lv416)\n",
       "            lv419: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv413, lv417)\n",
       "            lv420: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv419, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">146</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv421: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">147</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv422: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv418)\n",
       "            lv423: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv391, lv392)\n",
       "            lv424: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv420, lv421)\n",
       "            lv425: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv418, lv422)\n",
       "            lv426: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv425, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">148</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv427: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">149</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv428: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">40</span>, <span style=\"color: #008000\">40</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv423, lv424), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv429: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv426, lv427)\n",
       "            lv430: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv429)\n",
       "            lv431: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv429, lv430)\n",
       "            lv432: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv414, lv431)\n",
       "            lv433: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv432, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">150</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv434: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">151</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv435: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv433, lv434)\n",
       "            lv436: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv435)\n",
       "            lv437: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv435, lv436)\n",
       "            lv438: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv437, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">152</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv439: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">153</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv440: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv438, lv439)\n",
       "            lv441: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv440)\n",
       "            lv442: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv440, lv441)\n",
       "            lv443: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv432, lv442)\n",
       "            lv444: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv407, lv412)\n",
       "            lv445: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv443, lv444), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv446: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv445, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">154</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv447: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">155</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv448: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv446, lv447)\n",
       "            lv449: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv448)\n",
       "            lv450: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">128</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv448, lv449)\n",
       "            lv451: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">384</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv398, lv399, lv450), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv452: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv451, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">156</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv453: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">157</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv454: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv452, lv453)\n",
       "            lv455: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv454)\n",
       "            lv456: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv454, lv455)\n",
       "            lv457: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv456, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">158</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv458: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">159</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv459: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv456, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">160</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">256</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv460: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">161</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv461: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv457, lv458)\n",
       "            lv462: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv459, lv460)\n",
       "            lv463: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv461)\n",
       "            lv464: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv462)\n",
       "            lv465: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv461, lv463)\n",
       "            lv466: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv465, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">162</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv467: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">163</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv468: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">256</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv462, lv464)\n",
       "            lv469: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv468, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">164</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv470: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">165</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv471: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv466, lv467)\n",
       "            lv472: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv469, lv470)\n",
       "            lv473: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv471)\n",
       "            lv474: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv472)\n",
       "            lv475: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv471, lv473)\n",
       "            lv476: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv475, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">166</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv477: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">167</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv478: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv472, lv474)\n",
       "            lv479: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv478, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">168</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">80</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv480: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">169</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv481: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv479, lv480)\n",
       "            lv482: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv481)\n",
       "            lv483: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv481, lv482)\n",
       "            lv484: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv483, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">170</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv485: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">171</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv486: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv484, lv485)\n",
       "            lv487: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv486)\n",
       "            lv488: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv486, lv487)\n",
       "            lv489: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv488, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">172</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv490: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">173</span>], R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>]))\n",
       "            lv491: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv476, lv477)\n",
       "            lv492: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv489, lv490)\n",
       "            lv493: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">20</span>, <span style=\"color: #008000\">20</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv491, lv492), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv494: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">6400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv354, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">6400</span>]))\n",
       "            lv495: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">1600</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv428, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">1600</span>]))\n",
       "            lv496: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv493, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">400</span>]))\n",
       "            lv497: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">144</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv494, lv495, lv496), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">2</span>)\n",
       "            lv498 <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>call_tir(cls<span style=\"color: #AA22FF; font-weight: bold\">.</span>split5, (lv497,), out_sinfo<span style=\"color: #AA22FF; font-weight: bold\">=</span>[R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>), R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>)])\n",
       "            lv499: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">64</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv498[<span style=\"color: #008000\">0</span>]\n",
       "            lv500: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> lv498[<span style=\"color: #008000\">1</span>]\n",
       "            lv501: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv499, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">8400</span>]))\n",
       "            lv502: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>permute_dims(lv501, axes<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">3</span>])\n",
       "            lv503: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">16</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>softmax(lv502, axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv504: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>nn<span style=\"color: #AA22FF; font-weight: bold\">.</span>conv2d(lv503, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">174</span>], strides<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], padding<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>, <span style=\"color: #008000\">0</span>], dilation<span style=\"color: #AA22FF; font-weight: bold\">=</span>[<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">1</span>], groups<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>, data_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, kernel_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;OIHW&quot;</span>, out_layout<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;NCHW&quot;</span>, out_dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;void&quot;</span>)\n",
       "            lv505: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>reshape(lv504, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>shape([<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>]))\n",
       "            lv506: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>strided_slice(lv505, (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">1</span>),), (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">0</span>),), (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">2</span>),), (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">1</span>),), assume_inbound<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>)\n",
       "            lv507: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>strided_slice(lv505, (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">1</span>),), (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">2</span>),), (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">4</span>),), (R<span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_value(<span style=\"color: #008000\">1</span>),), assume_inbound<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000; font-weight: bold\">False</span>)\n",
       "            lv508: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>subtract(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">175</span>], lv506)\n",
       "            lv509: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">176</span>], lv507)\n",
       "            lv510: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>add(lv508, lv509)\n",
       "            lv511: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>divide(lv510, R<span style=\"color: #AA22FF; font-weight: bold\">.</span>const(<span style=\"color: #008000\">2.0</span>, <span style=\"color: #BA2121\">&quot;float32&quot;</span>))\n",
       "            lv512: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>subtract(lv509, lv508)\n",
       "            lv513: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv511, lv512), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            lv514: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>multiply(lv513, metadata[<span style=\"color: #BA2121\">&quot;relax.expr.Constant&quot;</span>][<span style=\"color: #008000\">177</span>])\n",
       "            lv515: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">80</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>sigmoid(lv500)\n",
       "            gv: R<span style=\"color: #AA22FF; font-weight: bold\">.</span>Tensor((<span style=\"color: #008000\">1</span>, <span style=\"color: #008000\">84</span>, <span style=\"color: #008000\">8400</span>), dtype<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;float32&quot;</span>) <span style=\"color: #AA22FF; font-weight: bold\">=</span> R<span style=\"color: #AA22FF; font-weight: bold\">.</span>concat((lv514, lv515), axis<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #008000\">1</span>)\n",
       "            R<span style=\"color: #AA22FF; font-weight: bold\">.</span>output(gv)\n",
       "        <span style=\"color: #008000; font-weight: bold\">return</span> gv\n",
       "\n",
       "<span style=\"color: #007979; font-style: italic\"># Metadata omitted. Use show_meta=True in script() method to show it.</span>\n",
       "</pre></div>\n"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    }
   ],
   "source": [
    "mod_actual.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "relax_func = tvm_model[\"main\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relax_func.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relax.op.split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvm_model.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# 将算子转换为推理模式\n",
    "tvm_model = relax.transform.DecomposeOpsForInference()(tvm_model)\n",
    "tvm_model.show()\n",
    "# 将任何 Relax 算子合法化为 TensorIR\n",
    "tvm_model = relax.transform.LegalizeOps()(tvm_model)\n",
    "\n",
    "# 将模型与参数分离\n",
    "tvm_model, params = relax.frontend.detach_params(tvm_model)\n",
    "# 将 Relax 图编译为虚拟机（VM）然后运行\n",
    "with tvm.transform.PassContext(opt_level=3):\n",
    "    ex = relax.build(tvm_model, target=\"llvm\")\n",
    "    vm = relax.VirtualMachine(ex, tvm.cpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准备输入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_list = [\n",
    "    inputs[key.name_hint] for key in tvm_model[\"main\"].params if key.name_hint in inputs\n",
    "]\n",
    "if params:\n",
    "    input_list += params[\"main\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行模型并检查输出："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vm.set_input(\"main\", *input_list)\n",
    "vm.invoke_stateful(\"main\")\n",
    "tvm_output = vm.get_outputs(\"main\")\n",
    "# 如果只有一个输出，则将其包装为列表\n",
    "if len(ort_output) == 1:\n",
    "    # 对于 TVM 不检查输出数量  \n",
    "    # 对于序列输出，TVM 的输出是元组（Tuple），  \n",
    "    # 而 ONNX 的输出数量是一个，即列表形式。\n",
    "    tvm_output = [tvm_output]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _check_output(tvm_out: list, ort_out: list, rtol: float = 1e-7, atol: float = 1e-5,):\n",
    "    if isinstance(tvm_out, tuple) and isinstance(ort_out, (tvm.runtime.ShapeTuple, list)):\n",
    "        assert len(tvm_out) == len(ort_out), \"Unequal number of outputs\"\n",
    "        for tvm_out_i, ort_out_i in zip(tvm_out, ort_out):\n",
    "            _check_output(tvm_out_i, ort_out_i)\n",
    "    elif isinstance(tvm_out, tvm.nd.NDArray) and isinstance(ort_out, np.ndarray):\n",
    "        np.testing.assert_allclose(tvm_out.numpy(), ort_out, rtol=rtol, atol=atol)\n",
    "    elif isinstance(tvm_out, tvm.runtime.ShapeTuple) and isinstance(ort_out, np.ndarray):\n",
    "        shape_out = tvm.nd.array([int(i) for i in tvm_out])\n",
    "        np.testing.assert_allclose(shape_out.numpy(), ort_out, rtol=rtol, atol=atol)\n",
    "    elif isinstance(tvm_out, (int, float, bool)) and isinstance(ort_out, np.ndarray):\n",
    "        np.testing.assert_allclose(np.array(tvm_out), ort_out, rtol=rtol, atol=atol)\n",
    "    else:\n",
    "        raise ValueError(f\"Unsupported types: {type(tvm_out)}, {type(ort_out)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check that number of outputs match.\n",
    "assert len(tvm_output) == len(ort_output), \"Unequal number of outputs\"\n",
    "for tvm_out, ort_out in zip(tvm_output, ort_output):\n",
    "    # TODO Allow configurable tolerance.\n",
    "    if ort_out is not None:\n",
    "        _check_output(tvm_out, ort_out, rtol=1e-4, atol=1e-5)"
   ]
  }
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